CMPSubset
Updates
Update 2021.08.11
Most thorough way to readdress differences between original and current analyses is to jsut redo the biomark. To that end, will restart analysis of just CMPm2, excluding other datasets (i.e., LSK, MEP, GMP). Will also keep
Notebook setup
Creating new pipeline using seurat v4.0.2 available 2021.06.08
Load libraries required for Seuratv4
library(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, unionlibrary(Seurat)Registered S3 methods overwritten by 'htmltools':
method from
print.html tools:rstudio
print.shiny.tag tools:rstudio
print.shiny.tag.list tools:rstudio
Registered S3 method overwritten by 'data.table':
method from
print.data.table
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
Attaching SeuratObjectlibrary(patchwork)
library(ggplot2)
library(clustree)Loading required package: ggraphSet global variables
projectName <- "CMP"
jackstraw.dim <- 40store session info
sessionInfo.filename <- paste0(projectName, "_sessionInfo.txt")
sink(sessionInfo.filename)
sessionInfo()
sink()source("../RFunctions/read_10XGenomics_data.R")
source("../RFunctions/PercentVariance.R")
source("../RFunctions/Mouse2Human_idconversion.R")
source ("../RFunctions/ColorPalette.R")setwd("../../cellRanger/") # temporarily changing wd only works if you run the entire chunk at once
data_file.list <- read_10XGenomics_data(sample.list = "CMPm2")
object.data <-Read10X(data_file.list)cmp.object<- CreateSeuratObject(counts = object.data, min.cells = 3, min.genes = 200, project = projectName)Clean up to free memory
remove(object.data)Add mitochondrial metadata and plot some basic features
cmp.object[["percent.mt"]] <- PercentageFeatureSet(cmp.object, pattern = "^mt-")
VlnPlot(cmp.object, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3, pt.size = 0, fill.by = 'orig.ident', )
plot1 <- FeatureScatter(cmp.object, feature1 = "nCount_RNA", feature2 = "percent.mt", group.by = "orig.ident", pt.size = 0.01)
plot2 <- FeatureScatter(cmp.object, feature1 = "nCount_RNA", feature2 = "nFeature_RNA", group.by = "orig.ident", pt.size = 0.01)
plot1 + plot2
Filter data
remove low quality cells require: nFeature_RNA between 200 and 4000 (inclusive) require: percent.mt <=5
print(paste("original object:", nrow(cmp.object@meta.data), "cells", sep = " "))[1] "original object: 12540 cells"cmp.object <- subset(cmp.object,
subset = nFeature_RNA >=200 &
nFeature_RNA <= 4000 &
percent.mt <= 5
)
print(paste("new object:", nrow(cmp.object@meta.data), "cells", sep = " "))[1] "new object: 12059 cells"cmp.object <- NormalizeData(cmp.object, normalization.method = "LogNormalize", scale.factor = 10000)Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|Find variable features
cmp.object <- FindVariableFeatures(cmp.object, selection.method = "vst", nfeatures = 2000)Calculating gene variances
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|top10 <- head(VariableFeatures(cmp.object), 10)
plot1 <- VariableFeaturePlot(cmp.object)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)When using repel, set xnudge and ynudge to 0 for optimal resultsplot1 + plot2
Scale data (linear transformation)
We don’t have to worry about comparing library depths, so we’ll just do normalization/Scale data
Regressing out nFeature_RNA, nCount_RNA
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Centering and scaling data matrix
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|================================================================================================================================================================================================================| 100%Save raw object
saveRDS(cmp.object, file = paste0(projectName, "_raw.RDS"))cmp.object <- RunPCA(cmp.object, features = VariableFeatures(cmp.object), ndims.print = 1:5, nfeatures.print = 5)PC_ 1
Positive: Vamp5, Nkg7, Car2, Apoe, Ctla2a
Negative: Lgals3, Aif1, Id2, Cst3, H2-Aa
PC_ 2
Positive: Prtn3, Ctsg, H2afy, Mpo, Emb
Negative: Ube2c, Cenpf, Nusap1, Mki67, Cenpa
PC_ 3
Positive: Pf4, Tmsb4x, Cavin2, Serpine2, Rap1b
Negative: Plac8, Cks2, Cenpa, Ube2c, Tubb4b
PC_ 4
Positive: Csrp3, Car1, Jun, Apoe, Jund
Negative: H2afz, Hmgn2, Birc5, Hmgb1, Tuba1b
PC_ 5
Positive: Pclaf, Tyms, Rrm2, Tk1, Pcna
Negative: Hist1h2bc, Tsc22d1, Smim14, Ccnb2, Serpinb1a DimPlot(cmp.object, reduction = "pca", group.by = "orig.ident")
VizDimLoadings(cmp.object, dims = 1:6, nfeatures = 10, reduction = "pca", ncol = 3)
Calculate dimensionality
ElbowPlot(cmp.object, ndims = 50)
percent.variance(cmp.object@reductions$pca@stdev)Number of PCs describing X% of variance
ElbowPlot(cmp.object, ndims = 50)
percent.variance(cmp.object@reductions$pca@stdev)
Add cluster IDs from Seurat v1
Exported cell IDs for clusters 3, 17, 10, 11 from Seurat v1. Will add these IDs as a metadata column.
Create column “clust.ID” and populate with 0’s. Then import IDs for clusters
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
paste0("Num pcs for 80% variance:", length(which(cumsum(tot.var) <= 80)))[1] "Num pcs for 80% variance:18"paste0("Num pcs for 85% variance:", length(which(cumsum(tot.var) <= 85)))[1] "Num pcs for 85% variance:25"paste0("Num pcs for 90% variance:", length(which(cumsum(tot.var) <= 90)))[1] "Num pcs for 90% variance:33"paste0("Num pcs for 95% variance:", length(which(cumsum(tot.var) <= 95)))[1] "Num pcs for 95% variance:41"Add new metadata column
clust3.cells <- read.table(file = "../Seuratv1_clusterCellIDs/cluster3cellIDs.txt", col.names = "clust03")
clust3.cells <- sapply(clust3.cells, function(x) paste0(gsub("CMP", "CMPm2", x), "-1"))
clust17.cells <- read.table(file = "../Seuratv1_clusterCellIDs/cluster17cellIDs.txt", col.names = "clust17")
clust17.cells <- sapply(clust17.cells, function(x) paste0(gsub("CMP", "CMPm2", x), "-1"))
clust10.cells <- read.table(file = "../Seuratv1_clusterCellIDs/cluster10cellIDs.txt", col.names = "clust10")
clust10.cells <- sapply(clust10.cells, function(x) paste0(gsub("CMP", "CMPm2", x), "-1"))
clust11.cells <- read.table(file = "../Seuratv1_clusterCellIDs/cluster11cellIDs.txt", col.names = "clust11")
clust11.cells <- sapply(clust11.cells, function(x) paste0(gsub("CMP", "CMPm2", x), "-1"))now map new ids
cmp.object@meta.data['clust.ID'] <- 0
head(cmp.object@meta.data)Registered S3 method overwritten by 'cli':
method from
print.boxx spatstat.geomdo numbers make sense?
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust3.cells] <- 3
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust17.cells] <- 17
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust10.cells] <- 10
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust11.cells] <- 11Total var 90%
Neighborhood and umap
set total.var <- 90%
nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 10,])[1] 1049nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 11,])[1] 1118nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 17,])[1] 883nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 3,])[1] 1931Plot UMAP
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 90))
cmp.object <- FindNeighbors(cmp.object, dims = 1:ndims)Computing nearest neighbor graphComputing SNNcmp.object <- FindClusters(cmp.object, resolution = 0.5)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 452999
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8502
Number of communities: 9
Elapsed time: 1 secondscmp.object <- RunUMAP(cmp.object, dims = 1: ndims)14:08:12 UMAP embedding parameters a = 0.9922 b = 1.112
14:08:12 Read 12059 rows and found 33 numeric columns
14:08:12 Using Annoy for neighbor search, n_neighbors = 30
14:08:12 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:08:13 Writing NN index file to temp file /var/folders/4f/fwrj6fnn1dn4g8wsf0zv563hjsvl24/T//Rtmp5gGDJN/file582a2b2d9d6c
14:08:14 Searching Annoy index using 1 thread, search_k = 3000
14:08:16 Annoy recall = 100%
14:08:17 Commencing smooth kNN distance calibration using 1 thread
14:08:17 Initializing from normalized Laplacian + noise
14:08:18 Commencing optimization for 200 epochs, with 513294 positive edges
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:08:23 Optimization finishedfor(x in c(0.5, 1, 1.5, 2, 2.5)){
cmp.object <- FindClusters(cmp.object, resolution = x)
}Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 452999
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8502
Number of communities: 9
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 452999
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7974
Number of communities: 17
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 452999
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7652
Number of communities: 22
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 452999
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7404
Number of communities: 27
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 452999
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7189
Number of communities: 30
Elapsed time: 1 secondssaveRDS(cmp.object, file = paste0(projectName, "_dim", ndims, ".RDS"))Clustree
what’s the max resolution we can achieve while keepign clusters stable?
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
myplot <- DimPlot(cmp.object,
group.by = meta.col,
reduction = "umap",
cols = color.palette
) +
ggtitle(paste0(projectName, " dim", ndims, "res", gsub("RNA_snn_res", "", meta.col) ))
plot(myplot)
}
}
I think I’m liking res.1.0 from this. Although how much does this change if I use fewer PCs…
for each resolution, number/percentage of cells in each cluster?
plot.title <- paste0(projectName, "_clustree_ndim", max(cmp.object@commands$RunUMAP.RNA.pca$dims))
my.clustree <- clustree(cmp.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability", exprs = "scale.data") +
scale_color_continuous(low = 'red3', high = 'white') +
ggtitle(plot.title)
png(filename = paste0(plot.title, ".png"), height = 800, width = 1600)
plot(my.clustree)
dev.off()null device
1 For each resolution, what percentage of cells in each cluster are enriched for one of our clust.IDs?
Test: what percentage of each new clusterID matches one of the older clusters?
tot.cells <- nrow(cmp.object@meta.data)
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
stats.df <- data.frame(matrix(ncol = 2, nrow = length(new.clusters)))
colnames(stats.df) <- c("num_cells", "pct_pop")
rownames(stats.df) <- new.clusters
meta.df <- cmp.object@meta.data
for(row.id in rownames(stats.df)){
num.x <- nrow(meta.df[meta.df[meta.col] == row.id,])
pct.x <- as.integer(num.x / tot.cells *100)
# print(pct.x)
stats.df[row.id, "num_cells"] <- num.x
stats.df[row.id, "pct_pop"] <- pct.x
}
print(stats.df)
}
}Absolutely terrible overlap, no enrichment of any of these across the new clustering algorithm. Maybe should try 95% variation covered
Find old cells on UMAP
time for the super scarey moment to see if the cells from seuratv1 still cluster together on in seurat v4
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
enrich.df <- data.frame(matrix(ncol = 4, nrow = length(new.clusters)))
colnames(enrich.df) <- c(3, 17, 10, 11)
rownames(enrich.df) <- new.clusters
meta.df <- cmp.object@meta.data
for(row.id in rownames(enrich.df)){
tot.clus <- nrow(meta.df[meta.df[[meta.col]] == row.id,])
for(col.id in colnames(enrich.df)){
num.x <- nrow(meta.df[(meta.df[[meta.col]] == row.id) & (meta.df$clust.ID == col.id),])
pct.x <- as.integer(num.x / tot.clus *100)
# print(pct.x)
enrich.df[row.id, col.id] <- pct.x
}
}
colnames(enrich.df) <- sapply(colnames(enrich.df), function(x) paste0("oldcluster", x))
rownames(enrich.df) <- sapply(rownames(enrich.df), function(x) paste0("newcluster", x))
xlsx::write.xlsx(enrich.df, file = paste0("PctOfNewClustersOverlappingOldClusters_", projectName, "_dim", ndims, ".xlsx"), sheetName = paste0(gsub("RNA_snn_", "", meta.col)), append = TRUE)
print(enrich.df)
}
}NADimPlot(cmp.object,
reduction = "umap",
group.by = "clust.ID",
# split.by = "orig.ident",
cols = c("gray", "orange", "blue", "red", "green"),)
Total var 85%
Neighborhood and umap
set total.var <- 85%
DimPlot(cmp.object,
reduction = "umap",
group.by = "orig.ident",
split.by = "clust.ID",
cols = c("gray", "orange", "blue", "red", "green"),)
cmp.object <- FindNeighbors(cmp.object, dims = 1:ndims)
cmp.object <- FindClusters(cmp.object, resolution = 0.5)
cmp.object <- RunUMAP(cmp.object, dims = 1: ndims)Plot UMAP
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 85))
cmp.object <- FindNeighbors(cmp.object, dims = 1:ndims)Computing nearest neighbor graphComputing SNNcmp.object <- FindClusters(cmp.object, resolution = 0.5)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 424091
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8508
Number of communities: 9
Elapsed time: 2 secondscmp.object <- RunUMAP(cmp.object, dims = 1: ndims)Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
10:37:55 UMAP embedding parameters a = 0.9922 b = 1.112
10:37:55 Read 12059 rows and found 25 numeric columns
10:37:55 Using Annoy for neighbor search, n_neighbors = 30
10:37:55 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:37:57 Writing NN index file to temp file /var/folders/4f/fwrj6fnn1dn4g8wsf0zv563hjsvl24/T//RtmpjY0Xgs/file912c3cf1e81b
10:37:57 Searching Annoy index using 1 thread, search_k = 3000
10:38:01 Annoy recall = 100%
10:38:01 Commencing smooth kNN distance calibration using 1 thread
10:38:02 Initializing from normalized Laplacian + noise
10:38:02 Commencing optimization for 200 epochs, with 500658 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|for(x in c(0.5, 1, 1.5, 2, 2.5)){
cmp.object <- FindClusters(cmp.object, resolution = x)
saveRDS(cmp.object, file = paste0(projectName, "_dim", ndims, ".RDS"))
}Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 424091
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8508
Number of communities: 9
Elapsed time: 3 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 424091
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8026
Number of communities: 18
Elapsed time: 2 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 424091
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7700
Number of communities: 22
Elapsed time: 2 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 424091
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7461
Number of communities: 28
Elapsed time: 2 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12059
Number of edges: 424091
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7257
Number of communities: 33
Elapsed time: 1 secondsClustree
what’s the max resolution we can achieve while keepign clusters stable?
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
plot.title <- paste0(projectName, "dim", ndims, "res", gsub("RNA_snn_res", "", meta.col))
myplot <- DimPlot(cmp.object,
group.by = meta.col,
reduction = "umap",
cols = color.palette
) +
ggtitle(plot.title)
plot(myplot)
png(filename = paste0(plot.title, ".png"), height = 800, width = 800)
plot(DimPlot(cmp.object,
group.by = meta.col,
reduction = "umap",
cols = color.palette,
pt.size = 1.5
) +
ggtitle(plot.title)
)
dev.off()
}
}
for each resolution, number/percentage of cells in each cluster?
tot.cells <- nrow(cmp.object@meta.data)
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
stats.df <- data.frame(matrix(ncol = 2, nrow = length(new.clusters)))
colnames(stats.df) <- c("num_cells", "pct_pop")
rownames(stats.df) <- new.clusters
meta.df <- cmp.object@meta.data
for(row.id in rownames(stats.df)){
num.x <- nrow(meta.df[meta.df[meta.col] == row.id,])
pct.x <- as.integer(num.x / tot.cells *100)
# print(pct.x)
stats.df[row.id, "num_cells"] <- num.x
stats.df[row.id, "pct_pop"] <- pct.x
}
print(stats.df)
}
}Identify variable genes for new biomark
tot.cells <- nrow(cmp.object@meta.data)
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
stats.df <- data.frame(matrix(ncol = 2, nrow = length(new.clusters)))
colnames(stats.df) <- c("num_cells", "pct_pop")
rownames(stats.df) <- new.clusters
meta.df <- cmp.object@meta.data
for(row.id in rownames(stats.df)){
num.x <- nrow(meta.df[meta.df[meta.col] == row.id,])
pct.x <- as.integer(num.x / tot.cells *100)
# print(pct.x)
stats.df[row.id, "num_cells"] <- num.x
stats.df[row.id, "pct_pop"] <- pct.x
}
print(stats.df)
}
}Gene profiles of clusters
set ident at res = 1 and get markers
Idents(cmp.object) <- "RNA_snn_res.1"
cmp.allmarkers.res1 <- FindAllMarkers(cmp.object)
cmp.top100markers.res1 <- cmp.allmarkers.res1 %>% group_by(cluster) %>% top_n(n = 100, wt = abs(avg_log2FC))Find top.var genes in cmp.top100markers.res1
top.var <- cmp.object@assays$RNA@var.features[1:150]
# top.var
n.vargenes <- c()
for(gene in top.var){
if(gene %in% cmp.top100markers.res1$gene){
n.vargenes <- c(n.vargenes, gene)
}
}
print(length(n.vargenes))
top.var[!(top.var %in% n.vargenes)]Lets try to export these, with genes on rows and clusters on columns, and xpression value in data table
var.df <- cmp.top100markers.res1[cmp.top100markers.res1$gene %in% n.vargenes,]
var.df <- subset.data.frame(var.df, select = c("gene", "cluster", "avg_log2FC"))
var.df <- reshape2::dcast(var.df, gene~cluster, value.var = "avg_log2FC")
xlsx::write.xlsx(var.df, file = paste0(projectName, "_dim", ndims, "_candididateBiomarkGenes.xlsx"), sheetName = "res1", append = TRUE, row.names = FALSE)
Here’s the list of genes, plus some controls
res1.biomark.genes <- c("Aif1",
"Aqp1",
"Birc5",
"Ccl3",
"Ccr2",
"Cd74",
"Cdc20",
"Cenpf",
"Cst3",
"Ctsh",
"Dntt",
"Elane",
"Ermap",
"F13a1",
"Fcer1g",
"Gm15915",
"Gm17590",
"H2-Aa",
"H2-Eb1",
"H2afx",
"H2afy",
"Hist1h2ac",
"Hmgb2",
"Hp",
"Ighm",
"Irf8",
"Lgals3",
"Lmo4",
"Ms4a2",
"Mt1",
"Plac8",
"Prtn3",
"Rap1b",
"Rgs1",
"Vwf",
"Wfdc17",
"Csrp3",
"Hist1h2ae",
"Ifitm1",
"Lgals1",
"Tmsb4x",
"Arl6ip1",
"Car2",
"Ccl9",
"Ccnb2",
"Cd9",
"Cenpa",
"Cpa3",
"Fos",
"Hist1h2ap",
"Ly6c2",
"Mpo",
"Pclaf",
"Slpi",
"Top2a",
"Ube2c",
"Ly86",
"Hist1h2bc",
"Pf4",
"Apoe",
"Ctsg",
"Car1",
"Hmmr",
"Gapdh",
"B2m",
"Actb",
"Hprt1",
"Gusb")make vln plots per cluster
res1.biomark.genes <- c("Aif1",
"Aqp1",
"Birc5",
"Ccl3",
"Ccr2",
"Cd74",
"Cdc20",
"Cenpf",
"Cst3",
"Ctsh",
"Dntt",
"Elane",
"Ermap",
"F13a1",
"Fcer1g",
"Gm15915",
"Gm17590",
"H2-Aa",
"H2-Eb1",
"H2afx",
"H2afy",
"Hist1h2ac",
"Hmgb2",
"Hp",
"Ighm",
"Irf8",
"Lgals3",
"Lmo4",
"Ms4a2",
"Mt1",
"Plac8",
"Prtn3",
"Rap1b",
"Rgs1",
"Vwf",
"Wfdc17",
"Csrp3",
"Hist1h2ae",
"Ifitm1",
"Lgals1",
"Tmsb4x",
"Arl6ip1",
"Car2",
"Ccl9",
"Ccnb2",
"Cd9",
"Cenpa",
"Cpa3",
"Fos",
"Hist1h2ap",
"Ly6c2",
"Mpo",
"Pclaf",
"Slpi",
"Top2a",
"Ube2c",
"Ly86",
"Hist1h2bc",
"Pf4",
"Apoe",
"Ctsg",
"Car1",
"Hmmr",
"Gapdh",
"B2m",
"Actb",
"Hprt1",
"Gusb")Res 1.5
repeat for top.var genes in cmp.top100markers.res1.5
for(gene in res1.biomark.genes){
plot.title <- paste0(projectName, "dim", ndims, "res1_", gene)
myplot<- VlnPlot(cmp.object, features = gene, pt.size = 0.01) + ggtitle(plot.title)
png(filename = paste0(plot.title, ".png"), height = 800, width = 1600)
plot(myplot)
dev.off()
}Error in FetchData(object = object, vars = features, slot = slot) :
None of the requested variables were found: Hprt1Lets try to export these, with genes on rows and clusters on columns, and xpression value in data table
var.df <- cmp.top100markers.res1.5[cmp.top100markers.res1.5$gene %in% n.vargenes,]
var.df <- subset.data.frame(var.df, select = c("gene", "cluster", "avg_log2FC"))
var.df <- reshape2::dcast(var.df, gene~cluster)
xlsx::write.xlsx(var.df, file = paste0(projectName, "_dim", ndims, "_candididateBiomarkGenes.xlsx"), sheetName = "res1.5", append = TRUE, row.names = FALSE)
For each resolution, what percentage of cells in each cluster are enriched for one of our clust.IDs?
Test: what percentage of each new clusterID matches one of the older clusters?
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
enrich.df <- data.frame(matrix(ncol = 4, nrow = length(new.clusters)))
colnames(enrich.df) <- c(3, 17, 10, 11)
rownames(enrich.df) <- new.clusters
meta.df <- cmp.object@meta.data
for(row.id in rownames(enrich.df)){
tot.clus <- nrow(meta.df[meta.df[[meta.col]] == row.id,])
for(col.id in colnames(enrich.df)){
num.x <- nrow(meta.df[(meta.df[[meta.col]] == row.id) & (meta.df$clust.ID == col.id),])
pct.x <- as.integer(num.x / tot.clus *100)
# print(pct.x)
enrich.df[row.id, col.id] <- pct.x
}
}
colnames(enrich.df) <- sapply(colnames(enrich.df), function(x) paste0("oldcluster", x))
rownames(enrich.df) <- sapply(rownames(enrich.df), function(x) paste0("newcluster", x))
xlsx::write.xlsx(enrich.df, file = paste0("PctOfNewClustersOverlappingOldClusters_", projectName, "_dim", ndims, ".xlsx"), sheetName = paste0(gsub("RNA_snn_", "", meta.col)), append = TRUE)
print(enrich.df)
}
}
Absolutely terrible overlap, no enrichment of any of these across the new clustering algorithm. Maybe should try 95% variation covered
Find old cells on UMAP
time for the super scarey moment to see if the cells from seuratv1 still cluster together on in seurat v4
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
enrich.df <- data.frame(matrix(ncol = 4, nrow = length(new.clusters)))
colnames(enrich.df) <- c(3, 17, 10, 11)
rownames(enrich.df) <- new.clusters
meta.df <- cmp.object@meta.data
for(row.id in rownames(enrich.df)){
tot.clus <- nrow(meta.df[meta.df[[meta.col]] == row.id,])
for(col.id in colnames(enrich.df)){
num.x <- nrow(meta.df[(meta.df[[meta.col]] == row.id) & (meta.df$clust.ID == col.id),])
pct.x <- as.integer(num.x / tot.clus *100)
# print(pct.x)
enrich.df[row.id, col.id] <- pct.x
}
}
colnames(enrich.df) <- sapply(colnames(enrich.df), function(x) paste0("oldcluster", x))
rownames(enrich.df) <- sapply(rownames(enrich.df), function(x) paste0("newcluster", x))
xlsx::write.xlsx(enrich.df, file = paste0("PctOfNewClustersOverlappingOldClusters_", projectName, "_dim", ndims, ".xlsx"), sheetName = paste0(gsub("RNA_snn_", "", meta.col)), append = TRUE)
print(enrich.df)
}
}WARNING: An illegal reflective access operation has occurred
WARNING: Illegal reflective access by org.apache.poi.util.SAXHelper (file:/Users/heustonef/Library/R/4.0/library/xlsxjars/java/poi-ooxml-3.10.1-20140818.jar) to constructor com.sun.org.apache.xerces.internal.util.SecurityManager()
WARNING: Please consider reporting this to the maintainers of org.apache.poi.util.SAXHelper
WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations
WARNING: All illegal access operations will be denied in a future releaseDimPlot(cmp.object,
reduction = "umap",
group.by = "clust.ID",
# split.by = "orig.ident",
cols = c("gray", "orange", "blue", "red", "green"),)
Gene expression of old clustrs on new map
Let’s see if we can get some gene expression profiles on these…
DimPlot(cmp.object,
reduction = "umap",
group.by = "orig.ident",
split.by = "clust.ID",
cols = c("gray", "orange", "blue", "red", "green"),)
Total var 80%
Neighborhood and umap
set total.var <- 80%
gene.list <- c("Gata1", "Gata2", "Pf4", "Dntt", "Mpo", "Meis1", "Irf8", "Elane", "Fli1", "Zfpm1")
VlnPlot(cmp.object, features = gene.list, group.by = "clust.ID", pt.size = 0.01, cols = c("gray", "orange", "blue", "red", "green"))
Plot UMAP
for(x in c(0.5, 1, 1.5, 2, 2.5)){
cmp.object <- FindClusters(cmp.object, resolution = x)
saveRDS(cmp.object, file = paste0(projectName, "_dim", ndims, ".RDS"))
}for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
myplot <- DimPlot(cmp.object,
group.by = meta.col,
reduction = "umap",
cols = color.palette
) +
ggtitle(paste0(projectName, " dim", ndims, "res", gsub("RNA_snn_res", "", meta.col) ))
plot(myplot)
}
}Clustree
what’s the max resolution we can achieve while keepign clusters stable?
plot.title <- paste0(projectName, "_clustree_ndim", max(cmp.object@commands$RunUMAP.RNA.pca$dims))
my.clustree <- clustree(cmp.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability", exprs = "scale.data") +
scale_color_continuous(low = 'red3', high = 'white') +
ggtitle(plot.title)
png(filename = paste0(plot.title, ".png"), height = 800, width = 1600)
plot(my.clustree)
dev.off()for each resolution, number/percentage of cells in each cluster?
tot.cells <- nrow(cmp.object@meta.data)
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
stats.df <- data.frame(matrix(ncol = 2, nrow = length(new.clusters)))
colnames(stats.df) <- c("num_cells", "pct_pop")
rownames(stats.df) <- new.clusters
meta.df <- cmp.object@meta.data
for(row.id in rownames(stats.df)){
num.x <- nrow(meta.df[meta.df[meta.col] == row.id,])
pct.x <- as.integer(num.x / tot.cells *100)
# print(pct.x)
stats.df[row.id, "num_cells"] <- num.x
stats.df[row.id, "pct_pop"] <- pct.x
}
print(stats.df)
}
}For each resolution, what percentage of cells in each cluster are enriched for one of our clust.IDs?
Test: what percentage of each new clusterID matches one of the older clusters?
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
enrich.df <- data.frame(matrix(ncol = 4, nrow = length(new.clusters)))
colnames(enrich.df) <- c(3, 17, 10, 11)
rownames(enrich.df) <- new.clusters
meta.df <- cmp.object@meta.data
for(row.id in rownames(enrich.df)){
tot.clus <- nrow(meta.df[meta.df[[meta.col]] == row.id,])
for(col.id in colnames(enrich.df)){
num.x <- nrow(meta.df[(meta.df[[meta.col]] == row.id) & (meta.df$clust.ID == col.id),])
pct.x <- as.integer(num.x / tot.clus *100)
# print(pct.x)
enrich.df[row.id, col.id] <- pct.x
}
}
colnames(enrich.df) <- sapply(colnames(enrich.df), function(x) paste0("oldcluster", x))
rownames(enrich.df) <- sapply(rownames(enrich.df), function(x) paste0("newcluster", x))
xlsx::write.xlsx(enrich.df, file = paste0("PctOfNewClustersOverlappingOldClusters_", projectName, "_dim", ndims, ".xlsx"), sheetName = paste0(gsub("RNA_snn_", "", meta.col)), append = TRUE)
print(enrich.df)
}
}
Absolutely terrible overlap, no enrichment of any of these across the new clustering algorithm. Maybe should try 95% variation covered
Find old cells on UMAP
time for the super scarey moment to see if the cells from seuratv1 still cluster together on in seurat v4
DimPlot(cmp.object,
reduction = "umap",
group.by = "clust.ID",
# split.by = "orig.ident",
cols = c("gray", "orange", "blue", "red", "green"),)DimPlot(cmp.object,
reduction = "umap",
group.by = "orig.ident",
split.by = "clust.ID",
cols = c("gray", "orange", "blue", "red", "green"),)Total var 95%
Neighborhood and umap
set total.var <- 95%
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 95))
cmp.object <- FindNeighbors(cmp.object, dims = 1:ndims)
cmp.object <- FindClusters(cmp.object, resolution = 0.5)
cmp.object <- RunUMAP(cmp.object, dims = 1: ndims)
Plot UMAP
for(x in c(0.5, 1, 1.5, 2, 2.5)){
cmp.object <- FindClusters(cmp.object, resolution = x)
}
saveRDS(cmp.object, file = paste0(projectName, "_dim", ndims, ".RDS"))cmp.object <- readRDS("CMP_dim41.RDS")plot.title <- paste0(projectName, "_clustree_ndim", max(cmp.object@commands$RunUMAP.RNA.pca$dims))
my.clustree <- clustree(cmp.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability", exprs = "scale.data") +
scale_color_continuous(low = 'red3', high = 'white') +
ggtitle(plot.title)
png(filename = paste0(plot.title, ".png"), height = 800, width = 1600)
plot(my.clustree)
dev.off()For each resolution, what percentage of cells in each cluster are enriched for one of our clust.IDs?
Test: what percentage of each new clusterID matches one of the older clusters?
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
enrich.df <- data.frame(matrix(ncol = 4, nrow = length(new.clusters)))
colnames(enrich.df) <- c(3, 17, 10, 11)
rownames(enrich.df) <- new.clusters
meta.df <- cmp.object@meta.data
for(row.id in rownames(enrich.df)){
tot.clus <- nrow(meta.df[meta.df[[meta.col]] == row.id,])
for(col.id in colnames(enrich.df)){
num.x <- nrow(meta.df[(meta.df[[meta.col]] == row.id) & (meta.df$clust.ID == col.id),])
pct.x <- as.integer(num.x / tot.clus *100)
# print(pct.x)
enrich.df[row.id, col.id] <- pct.x
}
}
colnames(enrich.df) <- sapply(colnames(enrich.df), function(x) paste0("oldcluster", x))
rownames(enrich.df) <- sapply(rownames(enrich.df), function(x) paste0("newcluster", x))
xlsx::write.xlsx(enrich.df, file = paste0("PctOfNewClustersOverlappingOldClusters_", projectName, "_dim", ndims, ".xlsx"), sheetName = paste0(gsub("RNA_snn_", "", meta.col)), append = TRUE)
print(enrich.df)
}
}
Absolutely terrible overlap, no enrichment of any of these across the new clustering algorithm. Maybe should try 95% variation covered
Find old cells on UMAP
time for the super scarey moment to see if the cells from seuratv1 still cluster together on in seurat v4
DimPlot(cmp.object,
reduction = "umap",
group.by = "clust.ID",
pt.size = .1,
# split.by = "orig.ident",
cols = c("gray", "orange", "blue", "red", "green"),)DimPlot(cmp.object,
reduction = "umap",
group.by = "orig.ident",
split.by = "clust.ID",
cols = c("gray", "orange", "blue", "red", "green"),)Gene expression of old clustrs on new map
Let’s see if we can get some gene expression profiles on these…
gene.list <- c("Gata1", "Gata2", "Pf4", "Dntt", "Mpo", "Meis1", "Irf8", "Elane", "Fli1", "Zfpm1")
VlnPlot(cmp.object, features = gene.list, group.by = "clust.ID", pt.size = 0.01, cols = c("gray", "orange", "blue", "red", "green"))Used the exce doc to do some fancy conditional formatting. Old cluster 17 is pretty dispersed until you it resolution 2.5. Otherise, cells in old cluster 17 do not constitute more than 40% of any cells in the new clusters.
As far as I can see, the two approaches are to do DGEof new CMP w/ resolution = 2.5, AND/OR do DGe using older cluster IDs. Sure seems to make sense to do both…
DGE w/ resolution = 2.5
Strt with comparing all clusters against all other clusters Write out cluster info
calculate FindAllMarkers() for different idents and save to new file
ident.list <- c("RNA_snn_res.0.5", "RNA_snn_res.1", "RNA_snn_res.1.5", "RNA_snn_res.2", "RNA_snn_res.2.5", "clust.ID")
for(tested.ident in ident.list){
Idents(cmp.object) <- tested.ident
all.markers <- FindAllMarkers(cmp.object)
xlsx::write.xlsx(x = all.markers[,c("avg_log2FC", "p_val_adj", "cluster", "gene")],
file = paste0(projectName, "_FindALLMarkers_res2.5.xlsx"),
sheetName = tested.ident,
col.names = TRUE,
row.names = FALSE,
append = TRUE)
}Create FindAllMarkers() lists for GSEA
Idents(cmp.object) <- "RNA_snn_res.2.5"
res.2.5.allmarkers <- FindAllMarkers(cmp.object)Map HGNC symbols
Mouse2HumanTable <- Mouse2Human(res.2.5.allmarkers$gene)
HGNC <- with(Mouse2HumanTable, Mouse2HumanTable$HGNC[match(res.2.5.allmarkers$gene, Mouse2HumanTable$MGI)])
head(res.2.5.allmarkers)
res.2.5.allmarkers$HGNC <- HGNC
tail(res.2.5.allmarkers)
sig.res.2.5 <- res.2.5.allmarkers[res.2.5.allmarkers$p_val_adj <= 0.05, ]
sig.res.2.5 <- sig.res.2.5[c("avg_log2FC", "HGNC", "cluster")]
sig.res.2.5 <- sig.res.2.5[!(sig.res.2.5$HGNC == "" | is.na(sig.res.2.5$HGNC)),] # GSEA will fail if there are any blanks or NAs in the table
sig.res.2.5 <- sig.res.2.5[]
for(cluster in unique(sig.res.2.5$cluster)){
print(paste("writing cluster", cluster))
new.table <- sig.res.2.5[sig.res.2.5$cluster == cluster, c("HGNC", "avg_log2FC")]
new.table <- new.table[order(-new.table$avg_log2FC), ]
write.table(new.table, file = paste0("RankList_res2.5_findAll_hgnc/res.2.5cluster", cluster, ".rnk"), quote = FALSE, row.names = FALSE, col.names = TRUE, sep = "\t", )
}calculate FindMarkers() that distinguish each cluster (might overlab between clusters)
ident.list <- c("RNA_snn_res.0.5", "RNA_snn_res.1", "RNA_snn_res.1.5", "RNA_snn_res.2", "RNA_snn_res.2.5", "clust.ID")
for(tested.ident in ident.list){
object.copy <- cmp.object
Idents(object.copy) <- tested.ident
print(paste("testing", tested.ident))
for (cluster in sort(as.numeric(levels(object.copy@meta.data[[tested.ident]])))){
print(paste("looking at cluster", cluster))
cluster.markers <- FindMarkers(object.copy, ident.1 = cluster)
try(
xlsx::write.xlsx(x = cluster.markers[,c("avg_log2FC", "p_val_adj")],
file = paste0(projectName, "_FindMarkers_", gsub("RNA_snn_", "", tested.ident), ".xlsx"),
sheetName = paste0("clst", cluster),
col.names = TRUE,
row.names = TRUE,
append = TRUE)
)
}
remove(object.copy)
}for (cluster in sort(as.numeric(levels(cmp.object@meta.data$RNA_snn_res.2.5)))){
cluster.markers <- FindMarkers(cmp.object, ident.1 = cluster)
xlsx::write.xlsx(x = cluster.markers[,c("avg_log2FC", "p_val_adj")],
file = paste0(projectName, "_FindMarkers_res2.5.xlsx"),
sheetName = paste0("clst", cluster),
col.names = TRUE,
row.names = TRUE,
append = TRUE)
}Combine clusters that might represent old cluster ids
DGE w/ metadata against clust.ID against “0”
reset ident as “clust.ID” and rerun FindAllMarkers()
Idents(cmp.object) <- "clust.ID"
all.markers <- FindAllMarkers(cmp.object)
xlsx::write.xlsx(x = all.markers[,c("avg_log2FC", "p_val_adj", "cluster", "gene")],
file = paste0(projectName, "_FindALLMarkers_clustID.xlsx"),
sheetName = "clustID",
col.names = TRUE,
row.names = FALSE,
append = TRUE)# Idents(cmp.object) <- "clust.ID"
for (cluster in unique(cmp.object@meta.data$clust.ID)){
print(cluster)
cluster.markers <- FindMarkers(cmp.object, ident.1 = cluster)
xlsx::write.xlsx(x = cluster.markers[,c("avg_log2FC", "p_val_adj")],
file = paste0(projectName, "_FindMarkers_clustID.xlsx"),
sheetName = paste0("oldclust", cluster),
col.names = TRUE,
row.names = TRUE,
append = TRUE)
}
Distinguishing features of clusters
Previously defined biomark genes based on PC contributions. Original list was based on all msAggr, but let’s see how CMP subset does?
VizDimLoadings(cmp.object, dims = 1:10, nfeatures = 30, reduction = "pca", ncol = 2)pca.df <- cmp.object[["pca"]]
pca.df <- as.data.frame(as.matrix(slot(object = pca.df, name = "feature.loadings")))
print(cmp.object[["pca"]], dims = 2, nfeatures = 5)
rownames(pca.df[pca.df$PC_2 %in% sort(pca.df$PC_2, decreasing = TRUE)[1:5], ])
rownames(pca.df[pca.df$PC_2 %in% sort(pca.df$PC_2)[1:5], ])now we can get a list of principal components!
first pull the list of oldAnalysis CMP top PC genes
cmp.biomark <- read.table(file = "/Users/heustonef/Desktop/CMPSubpops/BioMark/ProbePanels/CMP_PCTopGenes.txt", sep = "\t", header = TRUE)
biomark.cmptargets <- c()
for(df.col in 1:ncol(cmp.biomark)){
biomark.cmptargets <- c(biomark.cmptargets, biomark[,df.col])
}
print(colnames(biomark))
print(paste("total gene count:", length(biomark.cmptargets)))Now get the list of current pc gene trgets (oldAnalysis used ndim = 1:6, so we’ll start with that range)
pc.list <- c("PC_1", "PC_2", "PC_3", "PC_4", "PC_5", "PC_6")
pc.genes <- lapply(pc.list, function(x) rownames(pca.df[pca.df[[x]] %in% sort(pca.df[[x]], decreasing = TRUE)[1:30],])) #targeting roughly 180 genes like in biomark.cmptargets
pc.genes <- unique(unlist(pc.genes))
print(paste("total gene count:", length(pc.genes)))Now compare the lists, I guess:
# setdiff(x,y) gives you things in x not in y. setdiff(y,x) gives you things in y not in x
setdiff(biomark.cmptargets, pc.genes)
# print(paste("\n length:", length(setdiff(biomark.cmptargets, pc.genes))))
writeLines(c("", "length:", length(setdiff(biomark.cmptargets, pc.genes))))Umm, yeah that went kinda how I expected. Let’s do this again, but for the actual biomark gene lists.
biomark <- read.table(file = "/Users/heustonef/Desktop/CMPSubpops/BioMark/ProbePanels/BiomarkProbeList.txt", sep = "\t")
biomark <- biomark[,1]
setdiff(biomark, pc.genes)
writeLines(c("", "length:", length(setdiff(biomark, pc.genes))))What if we increase the number of pcs but decrease the depth of each? This might cover more of biomark, which was originally developed using msAggr instead of only the CMP subset
pc.list <- c("PC_1", "PC_2", "PC_3", "PC_4", "PC_5", "PC_6", "PC_7", "PC_8", "PC_9", "PC_10")
pc.genes <- lapply(pc.list, function(x) rownames(pca.df[pca.df[[x]] %in% sort(pca.df[[x]], decreasing = TRUE)[1:20],]))
pc.genes <- unique(unlist(pc.genes))
print(paste("total gene count:", length(pc.genes)))
setdiff(biomark, pc.genes)
writeLines(c("", "length:", length(setdiff(biomark, pc.genes))))For comparison, let’s just see how many of biomark.cmptargets were actually included in biomark
setdiff(biomark.cmptargets, biomark)
writeLines(c("", "length:", length(setdiff(biomark.cmptargets, pc.genes))))length(biomark) - length(setdiff(biomark, biomark.cmptargets))length(biomark) - length(setdiff(biomark, pc.genes))So when you look at it like that, it’s not actually that far off.
What are the similarities?:
setdiff(setdiff(biomark, biomark.cmptargets), setdiff(biomark, pc.genes))These are genes from the 97probes not in the old CMP set that are also not in the new CMP set. Other than Itga2b (which is a failed probe anyway), nothing screams. Also we’d have thrown Flt3 and Cd34 for in anyway because they’re requisite cell surface markers (also Flt3 surface marker is expensive but otherwise not noteworthy and not used in the current sorting strategy)
What about cell surface marker expression? * Cd34 * Cd16/32 * Cd9 * Cd41 * Cd48 * Sca1 (just throw that in for sh*&s and giggles)
surface.markers <- c("Cd34", "Fcgr3", "Fcgr2b", "Cd9", "Itga2b", "Cd48", "Ly6a")
FeaturePlot(cmp.object, features = surface.markers, pt.size = 1, split.by = "clust.ID", ncol = 1)Save as png
png(filename = "FeaturePlot_CMP_surfaceMarkers_clustIDfacet.png", height = 1600, width = 1600)
FeaturePlot(cmp.object, features = surface.markers, pt.size = 1, split.by = "clust.ID", ncol = 1)
dev.off()Compare @g hierarchcial clusteirng
Do clustering using biomark RNAs as input
# Read in BiomarkRNAs
biomark.rnas <- read.table('/Users/heustonef/Desktop/10XGenomicsData/BiomarkRNAs.txt')
biomark.rnas <- biomark.rnas$V1use biomark RNAs to define dimensional reduction
cmp.object <- readRDS("CMP_raw.RDS")
cmp.object <- RunPCA(cmp.object, features = biomark.rnas, ndims.print = 1:5, , nfeatures.print = 5)
ElbowPlot(cmp.object, ndims = 50)Now run the clustering
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 90))
cmp.object <- FindNeighbors(cmp.object, dims = 1:ndims)
cmp.object <- FindClusters(cmp.object, resolution = 0.5)
cmp.object <- RunUMAP(cmp.object, dims = 1: ndims)
find the clusters
for(x in c(0.5, 1, 1.5, 2, 2.5)){
cmp.object <- FindClusters(cmp.object, resolution = x)
}Plot the umaps and cell cluster ids
for (meta.col in colnames(cmp.object@meta.data)){
if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
myplot <- DimPlot(cmp.object,
group.by = meta.col,
reduction = "umap",
cols = color.palette
) +
ggtitle(paste0(projectName, " dim", ndims, "res", gsub("RNA_snn_res", "", meta.col) ))
plot(myplot)
}
}Calculate anticipated number of cells you’ll find in each biomark cluster
Get # cells in each cluster
tot.cellcount <- nrow(cmp.object@meta.data)
res05.list <- sort(unique(cmp.object@meta.data$RNA_snn_res.0.5), decreasing = FALSE)
sapply(res05.list,
function(x){
print(
paste(
"cluster", x, "=",
nrow(cmp.object@meta.data[cmp.object@meta.data$RNA_snn_res.0.5 == x,]),
"cells or",
round(nrow(cmp.object@meta.data[cmp.object@meta.data$RNA_snn_res.0.5 == x,])/tot.cellcount*100, digits = 2),
"% of total"
)
)
}
)So we did the dimensional reduction based on the biomark RNAs, then did our UMAP nearest neighbor clustering.
In the biomark hierarchcial clustering analysis I assayed 167 cells. The smallest cluster I detected had 3 cells, or 1.8% of total, and this is an uncomfortably small number of cells. Based on the UMAP calculations I would therefore expect to find 11 or 12 of the predicted 15 clusters. I found 12, and I don’t really like that last one, so 11 or 12. Since I did the hierarchcial clustering yesterday and did this math today, we can say it was independent of these results and therefore totally legit. Yay!!
---
title: "CMPSubset"
output: html_notebook
---

# Updates

## Update 2021.08.11
Most thorough way to readdress differences between original and current analyses is to jsut redo the biomark. To that end, will restart analysis of *just* CMPm2, excluding other datasets (i.e., LSK, MEP, GMP). Will also keep 


# Notebook setup

Creating new pipeline using seurat v4.0.2 available 2021.06.08

Load libraries required for Seuratv4

```{r setup}
library(dplyr)
library(Seurat)
library(patchwork)
library(ggplot2)
library(clustree)
```

# Set global variables
```{r}
projectName <- "CMP"
jackstraw.dim <- 40
```

store session info
```{r}
sessionInfo.filename <- paste0(projectName, "_sessionInfo.txt")
sink(sessionInfo.filename)
sessionInfo()
sink()
```


```{r}
source("../RFunctions/read_10XGenomics_data.R")
source("../RFunctions/PercentVariance.R")
source("../RFunctions/Mouse2Human_idconversion.R")
source ("../RFunctions/ColorPalette.R")
```


```{r warning=FALSE}
setwd("../../cellRanger/") # temporarily changing wd only works if you run the entire chunk at once
data_file.list <- read_10XGenomics_data(sample.list = "CMPm2")
object.data <-Read10X(data_file.list)
```



```{r}
cmp.object<- CreateSeuratObject(counts = object.data, min.cells = 3, min.genes = 200, project = projectName)
```

Clean up to free memory

```{r}
remove(object.data)
```


Add mitochondrial metadata and plot some basic features
```{r}
cmp.object[["percent.mt"]] <- PercentageFeatureSet(cmp.object, pattern = "^mt-")
VlnPlot(cmp.object, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3, pt.size = 0, fill.by = 'orig.ident', )
```


```{r}
plot1 <- FeatureScatter(cmp.object, feature1 = "nCount_RNA", feature2 = "percent.mt", group.by = "orig.ident", pt.size = 0.01)
plot2 <- FeatureScatter(cmp.object, feature1 = "nCount_RNA", feature2 = "nFeature_RNA", group.by = "orig.ident", pt.size = 0.01)
plot1 + plot2
```
## Filter data
remove low quality cells
require: nFeature_RNA between 200 and 4000 (inclusive)
require: percent.mt <=5

```{r}
print(paste("original object:", nrow(cmp.object@meta.data), "cells", sep = " "))
cmp.object <- subset(cmp.object, 
												subset = nFeature_RNA >=200 & 
													nFeature_RNA <= 4000 & 
													percent.mt <= 5
												)
print(paste("new object:", nrow(cmp.object@meta.data), "cells", sep = " "))
```



```{r}
cmp.object <- NormalizeData(cmp.object, normalization.method = "LogNormalize", scale.factor = 10000)
```


Find variable features
```{r fig.width = 5, fig.height = 2}
cmp.object <- FindVariableFeatures(cmp.object, selection.method = "vst", nfeatures = 2000)
top10 <- head(VariableFeatures(cmp.object), 10)
plot1 <- VariableFeaturePlot(cmp.object)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
plot1 + plot2
```

## Scale data (linear transformation)
We don't have to worry about comparing library depths, so we'll just do normalization/Scale data

```{r echo = FALSE}
all.genes <- rownames(cmp.object)
cmp.object <- ScaleData(cmp.object, features = all.genes, vars.to.regress = c("nFeature_RNA", "nCount_RNA"))
```

## Save raw object
```{r}
saveRDS(cmp.object, file = paste0(projectName, "_raw.RDS"))
```



```{r}
cmp.object <- RunPCA(cmp.object, features = VariableFeatures(cmp.object), ndims.print = 1:5, nfeatures.print = 5)
```

```{r}
DimPlot(cmp.object, reduction = "pca", group.by = "orig.ident")
VizDimLoadings(cmp.object, dims = 1:6, nfeatures = 10, reduction = "pca", ncol = 3)

```

Calculate dimensionality
```{r, figures-side, fig.show='hold', out.width="50%"}
ElbowPlot(cmp.object, ndims = 50)
percent.variance(cmp.object@reductions$pca@stdev)
```
Number of PCs describing X% of variance

```{r}
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
paste0("Num pcs for 80% variance:", length(which(cumsum(tot.var) <= 80)))
paste0("Num pcs for 85% variance:", length(which(cumsum(tot.var) <= 85)))
paste0("Num pcs for 90% variance:", length(which(cumsum(tot.var) <= 90)))
paste0("Num pcs for 95% variance:", length(which(cumsum(tot.var) <= 95)))

```

# Add cluster IDs from Seurat v1

Exported cell IDs for clusters 3, 17, 10, 11 from Seurat v1. Will add these IDs as a metadata column.  
Create column "clust.ID" and populate with 0's. Then import IDs for clusters



```{r}
clust3.cells <- read.table(file = "../Seuratv1_clusterCellIDs/cluster3cellIDs.txt", col.names = "clust03")
clust3.cells <- sapply(clust3.cells, function(x) paste0(gsub("CMP", "CMPm2", x), "-1"))
clust17.cells <- read.table(file = "../Seuratv1_clusterCellIDs/cluster17cellIDs.txt", col.names = "clust17")
clust17.cells <- sapply(clust17.cells, function(x) paste0(gsub("CMP", "CMPm2", x), "-1"))
clust10.cells <- read.table(file = "../Seuratv1_clusterCellIDs/cluster10cellIDs.txt", col.names = "clust10")
clust10.cells <- sapply(clust10.cells, function(x) paste0(gsub("CMP", "CMPm2", x), "-1"))
clust11.cells <- read.table(file = "../Seuratv1_clusterCellIDs/cluster11cellIDs.txt", col.names = "clust11")
clust11.cells <- sapply(clust11.cells, function(x) paste0(gsub("CMP", "CMPm2", x), "-1"))
```

Add new metadata column
```{r}
cmp.object@meta.data['clust.ID'] <- 0
head(cmp.object@meta.data)
```

now map new ids
```{r}
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust3.cells] <- 3
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust17.cells] <- 17
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust10.cells] <- 10
cmp.object@meta.data$clust.ID[rownames(cmp.object@meta.data) %in% clust11.cells] <- 11
```

do numbers make sense?
```{r}
nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 10,])
nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 11,])
nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 17,])
nrow(cmp.object@meta.data[cmp.object@meta.data$clust.ID == 3,])
```








# Total var 90%
## Neighborhood and umap
set total.var <- 90%
```{r}
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 90))

cmp.object <- FindNeighbors(cmp.object, dims = 1:ndims)
cmp.object <- FindClusters(cmp.object, resolution = 0.5)
cmp.object <- RunUMAP(cmp.object, dims = 1: ndims)

```
Plot UMAP

```{r}
for(x in c(0.5, 1, 1.5, 2, 2.5)){
	cmp.object <- FindClusters(cmp.object, resolution = x)
}
saveRDS(cmp.object, file = paste0(projectName, "_dim", ndims, ".RDS"))
```

```{r}
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		myplot <- DimPlot(cmp.object, 
											group.by = meta.col,
											reduction = "umap", 
											cols = color.palette
											) + 
			ggtitle(paste0(projectName, " dim", ndims, "res", gsub("RNA_snn_res", "", meta.col) ))
		plot(myplot)
	}
}
```

### Clustree
what's the max resolution we can achieve while keepign clusters stable?
```{r fig.width = 10, fig.height = 5}
plot.title <- paste0(projectName, "_clustree_ndim", max(cmp.object@commands$RunUMAP.RNA.pca$dims))
my.clustree <- clustree(cmp.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability", exprs = "scale.data") + 
	scale_color_continuous(low = 'red3', high = 'white') + 
	ggtitle(plot.title)
png(filename = paste0(plot.title, ".png"), height = 800, width = 1600)
plot(my.clustree)
dev.off()

```
I think I'm liking res.1.0 from this. Although how much does this change if I use fewer PCs...



for each resolution, number/percentage of cells in each cluster?

```{r}
tot.cells <- nrow(cmp.object@meta.data)
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
		stats.df <- data.frame(matrix(ncol = 2, nrow = length(new.clusters)))
		colnames(stats.df) <- c("num_cells", "pct_pop")
		rownames(stats.df) <- new.clusters
		meta.df <- cmp.object@meta.data
		for(row.id in rownames(stats.df)){
				num.x <- nrow(meta.df[meta.df[meta.col] == row.id,])
				pct.x <- as.integer(num.x / tot.cells *100)
				# print(pct.x)
				stats.df[row.id, "num_cells"] <- num.x
				stats.df[row.id, "pct_pop"] <- pct.x
		}
		print(stats.df)
	}
}
```



For each resolution, what percentage of cells in each cluster are enriched for one of our clust.IDs?


Test: what percentage of each new clusterID matches one of the older clusters?
```{r}
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
		enrich.df <- data.frame(matrix(ncol = 4, nrow = length(new.clusters)))
		colnames(enrich.df) <- c(3, 17, 10, 11)
		rownames(enrich.df) <- new.clusters
		meta.df <- cmp.object@meta.data
		for(row.id in rownames(enrich.df)){
			tot.clus <- nrow(meta.df[meta.df[[meta.col]] == row.id,])
			for(col.id in colnames(enrich.df)){
				num.x <- nrow(meta.df[(meta.df[[meta.col]] == row.id) & (meta.df$clust.ID == col.id),])
				pct.x <- as.integer(num.x / tot.clus *100)
				# print(pct.x)
				enrich.df[row.id, col.id] <- pct.x
			}
		}
		colnames(enrich.df) <- sapply(colnames(enrich.df), function(x) paste0("oldcluster", x))
		rownames(enrich.df) <- sapply(rownames(enrich.df), function(x) paste0("newcluster", x))
		xlsx::write.xlsx(enrich.df, file = paste0("PctOfNewClustersOverlappingOldClusters_", projectName, "_dim", ndims, ".xlsx"), sheetName = paste0(gsub("RNA_snn_", "", meta.col)), append = TRUE)
		print(enrich.df)
	}
}

```
Absolutely terrible overlap, no enrichment of any of these across the new clustering algorithm. Maybe should try 95% variation covered

## Find old cells on UMAP

time for the super scarey moment to see if the cells from seuratv1 still cluster together on in seurat v4

```{r fig.width = 4}
DimPlot(cmp.object,
				reduction = "umap",
				group.by = "clust.ID", 
				# split.by = "orig.ident",
				cols = c("gray", "orange", "blue", "red", "green"),)
```
```{r fig.width = 4}
DimPlot(cmp.object,
				reduction = "umap",
				group.by = "orig.ident", 
				split.by = "clust.ID",
				cols = c("gray", "orange", "blue", "red", "green"),)
```


# Total var 85%
## Neighborhood and umap
set total.var <- 85%
```{r}
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 85))
```

```{r}
cmp.object <- FindNeighbors(cmp.object, dims = 1:ndims)
cmp.object <- FindClusters(cmp.object, resolution = 0.5)
cmp.object <- RunUMAP(cmp.object, dims = 1: ndims)
```
Plot UMAP

```{r}
for(x in c(0.5, 1, 1.5, 2, 2.5)){
	cmp.object <- FindClusters(cmp.object, resolution = x)
saveRDS(cmp.object, file = paste0(projectName, "_dim", ndims, ".RDS"))
}
```

```{r}
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		plot.title <- paste0(projectName, "dim", ndims, "res", gsub("RNA_snn_res", "", meta.col))
		myplot <- DimPlot(cmp.object, 
											group.by = meta.col,
											reduction = "umap", 
											cols = color.palette
											) + 
			ggtitle(plot.title)
		plot(myplot)
		png(filename = paste0(plot.title, ".png"), height = 800, width = 800)
		plot(DimPlot(cmp.object, 
											group.by = meta.col,
											reduction = "umap", 
											cols = color.palette, 
											pt.size = 1.5
											) + 
			ggtitle(plot.title)
			)
		dev.off()
	}
}
```

### Clustree
what's the max resolution we can achieve while keepign clusters stable?
```{r fig.width = 10, fig.height = 5}
plot.title <- paste0(projectName, "_clustree_ndim", max(cmp.object@commands$RunUMAP.RNA.pca$dims))
my.clustree <- clustree(cmp.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability", exprs = "scale.data") + 
	scale_color_continuous(low = 'red3', high = 'white') + 
	ggtitle(plot.title)
png(filename = paste0(plot.title, ".png"), height = 800, width = 1600)
plot(my.clustree)
dev.off()
```



for each resolution, number/percentage of cells in each cluster?

```{r}
tot.cells <- nrow(cmp.object@meta.data)
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
		stats.df <- data.frame(matrix(ncol = 2, nrow = length(new.clusters)))
		colnames(stats.df) <- c("num_cells", "pct_pop")
		rownames(stats.df) <- new.clusters
		meta.df <- cmp.object@meta.data
		for(row.id in rownames(stats.df)){
				num.x <- nrow(meta.df[meta.df[meta.col] == row.id,])
				pct.x <- as.integer(num.x / tot.cells *100)
				# print(pct.x)
				stats.df[row.id, "num_cells"] <- num.x
				stats.df[row.id, "pct_pop"] <- pct.x
		}
		print(stats.df)
	}
}
```

## Identify variable genes for new biomark
```{r}
length(cmp.object@assays$RNA@var.features)
```





### Gene profiles of clusters
set ident at res = 1 and get markers
```{r}
Idents(cmp.object) <- "RNA_snn_res.1"
cmp.allmarkers.res1 <- FindAllMarkers(cmp.object)
cmp.top100markers.res1 <- cmp.allmarkers.res1 %>% group_by(cluster) %>% top_n(n = 100, wt = abs(avg_log2FC))
```

```{r}
cmp.top100markers.res1 <- cmp.allmarkers.res1 %>% group_by(cluster) %>% top_n(n = 100, wt = abs(avg_log2FC))
cmp.top100markers.res1 <- cmp.top100markers.res1[cmp.top100markers.res1$p_val_adj <= 0.05, ]
for(cluster in unique(cmp.top100markers.res1$cluster)){
	num.hits <- nrow(cmp.top100markers.res1[cmp.top100markers.res1$cluster == cluster, ])
	print(paste("cluster", cluster, "has", num.hits, "genes"))
}
```


Find top.var	genes in cmp.top100markers.res1
```{r}
top.var <- cmp.object@assays$RNA@var.features[1:150]
# top.var
n.vargenes <- c()
for(gene in top.var){
	if(gene %in% cmp.top100markers.res1$gene){
		n.vargenes <- c(n.vargenes, gene)
	}
}
print(length(n.vargenes))
top.var[!(top.var %in% n.vargenes)]
```


Lets try to export these, with genes on rows and clusters on columns, and xpression value in data table
```{r}
var.df <- cmp.top100markers.res1[cmp.top100markers.res1$gene %in% n.vargenes,]
var.df <- subset.data.frame(var.df, select = c("gene", "cluster", "avg_log2FC"))
var.df <- reshape2::dcast(var.df, gene~cluster, value.var = "avg_log2FC")
xlsx::write.xlsx(var.df, file = paste0(projectName, "_dim", ndims, "_candididateBiomarkGenes.xlsx"), sheetName = "res1", append = TRUE, row.names = FALSE)

```

Here's the list of genes, plus some controls
```{r}
res1.biomark.genes <- c("Aif1",
"Aqp1",
"Birc5",
"Ccl3",
"Ccr2",
"Cd74",
"Cdc20",
"Cenpf",
"Cst3",
"Ctsh",
"Dntt",
"Elane",
"Ermap",
"F13a1",
"Fcer1g",
"Gm15915",
"Gm17590",
"H2-Aa",
"H2-Eb1",
"H2afx",
"H2afy",
"Hist1h2ac",
"Hmgb2",
"Hp",
"Ighm",
"Irf8",
"Lgals3",
"Lmo4",
"Ms4a2",
"Mt1",
"Plac8",
"Prtn3",
"Rap1b",
"Rgs1",
"Vwf",
"Wfdc17",
"Csrp3",
"Hist1h2ae",
"Ifitm1",
"Lgals1",
"Tmsb4x",
"Arl6ip1",
"Car2",
"Ccl9",
"Ccnb2",
"Cd9",
"Cenpa",
"Cpa3",
"Fos",
"Hist1h2ap",
"Ly6c2",
"Mpo",
"Pclaf",
"Slpi",
"Top2a",
"Ube2c",
"Ly86",
"Hist1h2bc",
"Pf4",
"Apoe",
"Ctsg",
"Car1",
"Hmmr", 
"Gapdh", 
"B2m", 
"Actb", 
"Hprt1",
"Gusb")
```

make vln plots per cluster
```{r}
for(gene in res1.biomark.genes){
	plot.title <- paste0(projectName, "dim", ndims, "res1_", gene)
	myplot<- VlnPlot(cmp.object, features = gene, pt.size = 0.01) + ggtitle(plot.title)
	png(filename = paste0(plot.title, ".png"), height = 800, width = 1600)
	plot(myplot)
	dev.off()
	
}
```


## Res 1.5

repeat for top.var	genes in cmp.top100markers.res1.5
```{r}
top.var <- cmp.object@assays$RNA@var.features[1:150]
Idents(cmp.object) <- "RNA_snn_res.1.5"
cmp.allmarkers.res1.5 <- FindAllMarkers(cmp.object)
cmp.top100markers.res1.5 <- cmp.allmarkers.res1.5 %>% group_by(cluster) %>% top_n(n = 100, wt = abs(avg_log2FC))
n.vargenes <- c()
for(gene in top.var){
	if(gene %in% cmp.top100markers.res1.5$gene){
		n.vargenes <- c(n.vargenes, gene)
	}
}
print(length(n.vargenes))
top.var[!(top.var %in% n.vargenes)]
```


Lets try to export these, with genes on rows and clusters on columns, and xpression value in data table
```{r}
var.df <- cmp.top100markers.res1.5[cmp.top100markers.res1.5$gene %in% n.vargenes,]
var.df <- subset.data.frame(var.df, select = c("gene", "cluster", "avg_log2FC"))
var.df <- reshape2::dcast(var.df, gene~cluster)
xlsx::write.xlsx(var.df, file = paste0(projectName, "_dim", ndims, "_candididateBiomarkGenes.xlsx"), sheetName = "res1.5", append = TRUE, row.names = FALSE)

```
For each resolution, what percentage of cells in each cluster are enriched for one of our clust.IDs?


Test: what percentage of each new clusterID matches one of the older clusters?
```{r}
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
		enrich.df <- data.frame(matrix(ncol = 4, nrow = length(new.clusters)))
		colnames(enrich.df) <- c(3, 17, 10, 11)
		rownames(enrich.df) <- new.clusters
		meta.df <- cmp.object@meta.data
		for(row.id in rownames(enrich.df)){
			tot.clus <- nrow(meta.df[meta.df[[meta.col]] == row.id,])
			for(col.id in colnames(enrich.df)){
				num.x <- nrow(meta.df[(meta.df[[meta.col]] == row.id) & (meta.df$clust.ID == col.id),])
				pct.x <- as.integer(num.x / tot.clus *100)
				# print(pct.x)
				enrich.df[row.id, col.id] <- pct.x
			}
		}
		colnames(enrich.df) <- sapply(colnames(enrich.df), function(x) paste0("oldcluster", x))
		rownames(enrich.df) <- sapply(rownames(enrich.df), function(x) paste0("newcluster", x))
		xlsx::write.xlsx(enrich.df, file = paste0("PctOfNewClustersOverlappingOldClusters_", projectName, "_dim", ndims, ".xlsx"), sheetName = paste0(gsub("RNA_snn_", "", meta.col)), append = TRUE)
		print(enrich.df)
	}
}

```
Absolutely terrible overlap, no enrichment of any of these across the new clustering algorithm. Maybe should try 95% variation covered

## Find old cells on UMAP

time for the super scarey moment to see if the cells from seuratv1 still cluster together on in seurat v4

```{r fig.width = 4}
DimPlot(cmp.object,
				reduction = "umap",
				group.by = "clust.ID", 
				# split.by = "orig.ident",
				cols = c("gray", "orange", "blue", "red", "green"),)
```
```{r fig.width = 4}
DimPlot(cmp.object,
				reduction = "umap",
				group.by = "orig.ident", 
				split.by = "clust.ID",
				cols = c("gray", "orange", "blue", "red", "green"),)
```

### Gene expression of old clustrs on new map
Let's see if we can get some gene expression profiles on these...
```{r, fig.height=10, fig.width=18}
gene.list <- c("Gata1", "Gata2", "Pf4", "Dntt", "Mpo", "Meis1", "Irf8", "Elane", "Fli1", "Zfpm1")
VlnPlot(cmp.object, features = gene.list, group.by = "clust.ID", pt.size = 0.01, cols = c("gray", "orange", "blue", "red", "green"))
```


# Total var 80%
## Neighborhood and umap
set total.var <- 80%
```{r}
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 80))

cmp.object <- FindNeighbors(cmp.object, dims = 1:ndims)
cmp.object <- FindClusters(cmp.object, resolution = 0.5)
cmp.object <- RunUMAP(cmp.object, dims = 1: ndims)

```
Plot UMAP

```{r}
for(x in c(0.5, 1, 1.5, 2, 2.5)){
	cmp.object <- FindClusters(cmp.object, resolution = x)
saveRDS(cmp.object, file = paste0(projectName, "_dim", ndims, ".RDS"))
}
```

```{r}
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		myplot <- DimPlot(cmp.object, 
											group.by = meta.col,
											reduction = "umap", 
											cols = color.palette
											) + 
			ggtitle(paste0(projectName, " dim", ndims, "res", gsub("RNA_snn_res", "", meta.col) ))
		plot(myplot)
	}
}
```

### Clustree
what's the max resolution we can achieve while keepign clusters stable?
```{r fig.width = 10, fig.height = 5}
plot.title <- paste0(projectName, "_clustree_ndim", max(cmp.object@commands$RunUMAP.RNA.pca$dims))
my.clustree <- clustree(cmp.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability", exprs = "scale.data") + 
	scale_color_continuous(low = 'red3', high = 'white') + 
	ggtitle(plot.title)
png(filename = paste0(plot.title, ".png"), height = 800, width = 1600)
plot(my.clustree)
dev.off()
```



for each resolution, number/percentage of cells in each cluster?

```{r}
tot.cells <- nrow(cmp.object@meta.data)
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
		stats.df <- data.frame(matrix(ncol = 2, nrow = length(new.clusters)))
		colnames(stats.df) <- c("num_cells", "pct_pop")
		rownames(stats.df) <- new.clusters
		meta.df <- cmp.object@meta.data
		for(row.id in rownames(stats.df)){
				num.x <- nrow(meta.df[meta.df[meta.col] == row.id,])
				pct.x <- as.integer(num.x / tot.cells *100)
				# print(pct.x)
				stats.df[row.id, "num_cells"] <- num.x
				stats.df[row.id, "pct_pop"] <- pct.x
		}
		print(stats.df)
	}
}
```



For each resolution, what percentage of cells in each cluster are enriched for one of our clust.IDs?


Test: what percentage of each new clusterID matches one of the older clusters?
```{r}
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
		enrich.df <- data.frame(matrix(ncol = 4, nrow = length(new.clusters)))
		colnames(enrich.df) <- c(3, 17, 10, 11)
		rownames(enrich.df) <- new.clusters
		meta.df <- cmp.object@meta.data
		for(row.id in rownames(enrich.df)){
			tot.clus <- nrow(meta.df[meta.df[[meta.col]] == row.id,])
			for(col.id in colnames(enrich.df)){
				num.x <- nrow(meta.df[(meta.df[[meta.col]] == row.id) & (meta.df$clust.ID == col.id),])
				pct.x <- as.integer(num.x / tot.clus *100)
				# print(pct.x)
				enrich.df[row.id, col.id] <- pct.x
			}
		}
		colnames(enrich.df) <- sapply(colnames(enrich.df), function(x) paste0("oldcluster", x))
		rownames(enrich.df) <- sapply(rownames(enrich.df), function(x) paste0("newcluster", x))
		xlsx::write.xlsx(enrich.df, file = paste0("PctOfNewClustersOverlappingOldClusters_", projectName, "_dim", ndims, ".xlsx"), sheetName = paste0(gsub("RNA_snn_", "", meta.col)), append = TRUE)
		print(enrich.df)
	}
}

```
Absolutely terrible overlap, no enrichment of any of these across the new clustering algorithm. Maybe should try 95% variation covered

## Find old cells on UMAP

time for the super scarey moment to see if the cells from seuratv1 still cluster together on in seurat v4

```{r fig.width = 4}
DimPlot(cmp.object,
				reduction = "umap",
				group.by = "clust.ID", 
				# split.by = "orig.ident",
				cols = c("gray", "orange", "blue", "red", "green"),)
```
```{r fig.width = 4}
DimPlot(cmp.object,
				reduction = "umap",
				group.by = "orig.ident", 
				split.by = "clust.ID",
				cols = c("gray", "orange", "blue", "red", "green"),)
```



# Total var 95%

## Neighborhood and umap
set total.var <- 95%
```{r}
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 95))

cmp.object <- FindNeighbors(cmp.object, dims = 1:ndims)
cmp.object <- FindClusters(cmp.object, resolution = 0.5)
cmp.object <- RunUMAP(cmp.object, dims = 1: ndims)

```
Plot UMAP

```{r}
for(x in c(0.5, 1, 1.5, 2, 2.5)){
	cmp.object <- FindClusters(cmp.object, resolution = x)
}
saveRDS(cmp.object, file = paste0(projectName, "_dim", ndims, ".RDS"))
```

```{r}
cmp.object <- readRDS("CMP_dim41.RDS")
```


```{r fig.width = 10, fig.height = 5}
plot.title <- paste0(projectName, "_clustree_ndim", max(cmp.object@commands$RunUMAP.RNA.pca$dims))
my.clustree <- clustree(cmp.object, prefix = "RNA_snn_res.", node_colour = "sc3_stability", exprs = "scale.data") + 
	scale_color_continuous(low = 'red3', high = 'white') + 
	ggtitle(plot.title)
png(filename = paste0(plot.title, ".png"), height = 800, width = 1600)
plot(my.clustree)
dev.off()
```

For each resolution, what percentage of cells in each cluster are enriched for one of our clust.IDs?


Test: what percentage of each new clusterID matches one of the older clusters?
```{r}
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		new.clusters <- sort(as.numeric(levels(cmp.object@meta.data[[meta.col]])))
		enrich.df <- data.frame(matrix(ncol = 4, nrow = length(new.clusters)))
		colnames(enrich.df) <- c(3, 17, 10, 11)
		rownames(enrich.df) <- new.clusters
		meta.df <- cmp.object@meta.data
		for(row.id in rownames(enrich.df)){
			tot.clus <- nrow(meta.df[meta.df[[meta.col]] == row.id,])
			for(col.id in colnames(enrich.df)){
				num.x <- nrow(meta.df[(meta.df[[meta.col]] == row.id) & (meta.df$clust.ID == col.id),])
				pct.x <- as.integer(num.x / tot.clus *100)
				# print(pct.x)
				enrich.df[row.id, col.id] <- pct.x
			}
		}
		colnames(enrich.df) <- sapply(colnames(enrich.df), function(x) paste0("oldcluster", x))
		rownames(enrich.df) <- sapply(rownames(enrich.df), function(x) paste0("newcluster", x))
		xlsx::write.xlsx(enrich.df, file = paste0("PctOfNewClustersOverlappingOldClusters_", projectName, "_dim", ndims, ".xlsx"), sheetName = paste0(gsub("RNA_snn_", "", meta.col)), append = TRUE)
		print(enrich.df)
	}
}

```
Absolutely terrible overlap, no enrichment of any of these across the new clustering algorithm. Maybe should try 95% variation covered

## Find old cells on UMAP

time for the super scarey moment to see if the cells from seuratv1 still cluster together on in seurat v4

```{r fig.width = 2}
DimPlot(cmp.object,
				reduction = "umap",
				group.by = "clust.ID", 
				pt.size = .1,
				# split.by = "orig.ident",
				cols = c("gray", "orange", "blue", "red", "green"),)
```
```{r fig.width = 4}
DimPlot(cmp.object,
				reduction = "umap",
				group.by = "orig.ident", 
				split.by = "clust.ID",
				cols = c("gray", "orange", "blue", "red", "green"),)
```



### Gene expression of old clustrs on new map
Let's see if we can get some gene expression profiles on these...
```{r, fig.height=10, fig.width=18}
gene.list <- c("Gata1", "Gata2", "Pf4", "Dntt", "Mpo", "Meis1", "Irf8", "Elane", "Fli1", "Zfpm1")
VlnPlot(cmp.object, features = gene.list, group.by = "clust.ID", pt.size = 0.01, cols = c("gray", "orange", "blue", "red", "green"))
```


Used the exce doc to do some fancy conditional formatting. Old cluster 17 is pretty dispersed until you it resolution 2.5. Otherise, cells in old cluster 17 do not constitute more than 40% of any cells in the new clusters.  
As far as I can see, the two approaches are to do DGEof new CMP w/ resolution = 2.5, AND/OR do DGe using older cluster IDs. Sure seems to make sense to do both...


# DGE w/ resolution = 2.5
Strt with comparing all clusters against all other clusters
Write out cluster info


calculate `FindAllMarkers()` for different idents and save to new file
```{r}
ident.list <- c("RNA_snn_res.0.5", "RNA_snn_res.1", "RNA_snn_res.1.5", "RNA_snn_res.2", "RNA_snn_res.2.5", "clust.ID")
for(tested.ident in ident.list){
	Idents(cmp.object) <- tested.ident
	all.markers <- FindAllMarkers(cmp.object)
	xlsx::write.xlsx(x = all.markers[,c("avg_log2FC", "p_val_adj", "cluster", "gene")], 
									 file = paste0(projectName, "_FindALLMarkers_res2.5.xlsx"), 
									 sheetName = tested.ident, 
									 col.names = TRUE, 
									 row.names = FALSE, 
									 append = TRUE)
}
```

Create `FindAllMarkers()` lists for GSEA
```{r }
Idents(cmp.object) <- "RNA_snn_res.2.5"
res.2.5.allmarkers <- FindAllMarkers(cmp.object)
```

## Map HGNC symbols
```{r}
Mouse2HumanTable <- Mouse2Human(res.2.5.allmarkers$gene)

HGNC <- with(Mouse2HumanTable, Mouse2HumanTable$HGNC[match(res.2.5.allmarkers$gene, Mouse2HumanTable$MGI)])
head(res.2.5.allmarkers)
res.2.5.allmarkers$HGNC <- HGNC
tail(res.2.5.allmarkers)
sig.res.2.5 <- res.2.5.allmarkers[res.2.5.allmarkers$p_val_adj <= 0.05, ]
sig.res.2.5 <- sig.res.2.5[c("avg_log2FC", "HGNC", "cluster")]
sig.res.2.5 <- sig.res.2.5[!(sig.res.2.5$HGNC == "" | is.na(sig.res.2.5$HGNC)),] # GSEA will fail if there are any blanks or NAs in the table
sig.res.2.5 <- sig.res.2.5[]

```


```{r}
for(cluster in unique(sig.res.2.5$cluster)){
	print(paste("writing cluster", cluster))
	new.table <- sig.res.2.5[sig.res.2.5$cluster == cluster, c("HGNC", "avg_log2FC")]
	new.table <- new.table[order(-new.table$avg_log2FC), ]
	write.table(new.table, file = paste0("RankList_res2.5_findAll_hgnc/res.2.5cluster", cluster, ".rnk"), quote = FALSE, row.names = FALSE, col.names = TRUE, sep = "\t", )
	
}
```



calculate `FindMarkers()` that distinguish each cluster (might overlab between clusters)
```{r}
ident.list <- c("RNA_snn_res.0.5", "RNA_snn_res.1", "RNA_snn_res.1.5", "RNA_snn_res.2", "RNA_snn_res.2.5", "clust.ID")
for(tested.ident in ident.list){
	object.copy <- cmp.object
	Idents(object.copy) <- tested.ident
	print(paste("testing", tested.ident))
	for (cluster in sort(as.numeric(levels(object.copy@meta.data[[tested.ident]])))){
		print(paste("looking at cluster", cluster))
		cluster.markers <- FindMarkers(object.copy, ident.1 = cluster)
		try(
			xlsx::write.xlsx(x = cluster.markers[,c("avg_log2FC", "p_val_adj")], 
											 file = paste0(projectName, "_FindMarkers_", gsub("RNA_snn_", "", tested.ident), ".xlsx"), 
											 sheetName = paste0("clst", cluster), 
											 col.names = TRUE, 
											 row.names = TRUE, 
											 append = TRUE)
		)	
	}
	remove(object.copy)
}
```



```{r}
for (cluster in sort(as.numeric(levels(cmp.object@meta.data$RNA_snn_res.2.5)))){
	cluster.markers <- FindMarkers(cmp.object, ident.1 = cluster)
	xlsx::write.xlsx(x = cluster.markers[,c("avg_log2FC", "p_val_adj")], 
									 file = paste0(projectName, "_FindMarkers_res2.5.xlsx"), 
									 sheetName = paste0("clst", cluster), 
									 col.names = TRUE, 
									 row.names = TRUE, 
									 append = TRUE)
}
```

## Combine clusters that might represent old cluster ids

# DGE w/ metadata against clust.ID against "0"
reset ident as "clust.ID" and rerun `FindAllMarkers()`
```{r}
	Idents(cmp.object) <- "clust.ID"
	all.markers <- FindAllMarkers(cmp.object)
	xlsx::write.xlsx(x = all.markers[,c("avg_log2FC", "p_val_adj", "cluster", "gene")], 
									 file = paste0(projectName, "_FindALLMarkers_clustID.xlsx"), 
									 sheetName = "clustID", 
									 col.names = TRUE, 
									 row.names = FALSE, 
									 append = TRUE)
```


```{r}
# Idents(cmp.object) <- "clust.ID"
for (cluster in unique(cmp.object@meta.data$clust.ID)){
	print(cluster)
	cluster.markers <- FindMarkers(cmp.object, ident.1 = cluster)
	xlsx::write.xlsx(x = cluster.markers[,c("avg_log2FC", "p_val_adj")], 
									 file = paste0(projectName, "_FindMarkers_clustID.xlsx"), 
									 sheetName = paste0("oldclust", cluster), 
									 col.names = TRUE, 
									 row.names = TRUE, 
									 append = TRUE)
}

```


# Distinguishing features of clusters
Previously defined biomark genes based on PC contributions. Original list was based on *all* msAggr, but let's see how CMP subset does?
```{r fig.height = 30, fig.width=6}
VizDimLoadings(cmp.object, dims = 1:10, nfeatures = 30, reduction = "pca", ncol = 2)
```

```{r}
pca.df <- cmp.object[["pca"]]
pca.df <- as.data.frame(as.matrix(slot(object = pca.df, name = "feature.loadings")))
print(cmp.object[["pca"]], dims = 2, nfeatures = 5)
rownames(pca.df[pca.df$PC_2 %in% sort(pca.df$PC_2, decreasing = TRUE)[1:5], ])
rownames(pca.df[pca.df$PC_2 %in% sort(pca.df$PC_2)[1:5], ])
```

now we can get a list of principal components!  
first pull the list of oldAnalysis CMP top PC genes
```{r}
cmp.biomark <- read.table(file = "/Users/heustonef/Desktop/CMPSubpops/BioMark/ProbePanels/CMP_PCTopGenes.txt", sep = "\t", header = TRUE)
biomark.cmptargets <- c()
for(df.col in 1:ncol(cmp.biomark)){
	biomark.cmptargets <- c(biomark.cmptargets, biomark[,df.col])
}
print(colnames(biomark))
print(paste("total gene count:", length(biomark.cmptargets)))
```

Now get the list of current pc gene trgets (oldAnalysis used ndim = 1:6, so we'll start with that range)
```{r}
pc.list <- c("PC_1", "PC_2", "PC_3", "PC_4", "PC_5", "PC_6")
pc.genes <- lapply(pc.list, function(x) rownames(pca.df[pca.df[[x]] %in% sort(pca.df[[x]], decreasing = TRUE)[1:30],])) #targeting roughly 180 genes like in biomark.cmptargets
pc.genes <- unique(unlist(pc.genes))
print(paste("total gene count:", length(pc.genes)))
```

Now compare the lists, I guess:

```{r}
# setdiff(x,y) gives you things in x not in y. setdiff(y,x) gives you things in y not in x
setdiff(biomark.cmptargets, pc.genes)
# print(paste("\n length:", length(setdiff(biomark.cmptargets, pc.genes))))
writeLines(c("", "length:", length(setdiff(biomark.cmptargets, pc.genes))))
```
Umm, yeah that went kinda how I expected. Let's do this again, but for the actual biomark gene lists.
```{r}
biomark <- read.table(file = "/Users/heustonef/Desktop/CMPSubpops/BioMark/ProbePanels/BiomarkProbeList.txt", sep = "\t")
biomark <- biomark[,1]
setdiff(biomark, pc.genes)
writeLines(c("", "length:", length(setdiff(biomark, pc.genes))))
```


What if we increase the number of pcs but decrease the depth of each? This might cover more of `biomark	`, which was originally developed using msAggr instead of only the CMP subset
```{r}
pc.list <- c("PC_1", "PC_2", "PC_3", "PC_4", "PC_5", "PC_6", "PC_7", "PC_8", "PC_9", "PC_10")
pc.genes <- lapply(pc.list, function(x) rownames(pca.df[pca.df[[x]] %in% sort(pca.df[[x]], decreasing = TRUE)[1:20],]))
pc.genes <- unique(unlist(pc.genes))
print(paste("total gene count:", length(pc.genes)))

```
```{r}
setdiff(biomark, pc.genes)
writeLines(c("", "length:", length(setdiff(biomark, pc.genes))))
```

For comparison, let's just see how many of `biomark.cmptargets` were actually included in `biomark`
```{r}
setdiff(biomark.cmptargets, biomark)
writeLines(c("", "length:", length(setdiff(biomark.cmptargets, pc.genes))))
```
```{r}
length(biomark) - length(setdiff(biomark, biomark.cmptargets))
```
```{r}
length(biomark) - length(setdiff(biomark, pc.genes))
```
So when you look at it like that, it's not actually that far off.


What are the similarities?:
```{r}
setdiff(setdiff(biomark, biomark.cmptargets), setdiff(biomark, pc.genes))
```
These are genes from the 97probes not in the old CMP set that are also not in the new CMP set. Other than Itga2b (which is a failed probe anyway), nothing screams. Also we'd have thrown Flt3 and Cd34 for in anyway because they're requisite cell surface markers (also Flt3 surface marker is expensive but otherwise not noteworthy and not used in the current sorting strategy)

What about cell surface marker expression?
* Cd34
* Cd16/32
* Cd9
* Cd41
* Cd48
* Sca1 (just throw that in for sh*&s and giggles)
```{r, fig.height = 15, fig.width=10}
surface.markers <- c("Cd34", "Fcgr3", "Fcgr2b", "Cd9", "Itga2b", "Cd48", "Ly6a")
FeaturePlot(cmp.object, features = surface.markers, pt.size = 1, split.by = "clust.ID", ncol = 1)
```
Save as png
```{r}
png(filename = "FeaturePlot_CMP_surfaceMarkers_clustIDfacet.png", height = 1600, width = 1600)
FeaturePlot(cmp.object, features = surface.markers, pt.size = 1, split.by = "clust.ID", ncol = 1)
dev.off()
```







# Compare @g hierarchcial clusteirng

Do clustering using biomark RNAs as input
```{r}
# Read in BiomarkRNAs
biomark.rnas <- read.table('/Users/heustonef/Desktop/10XGenomicsData/BiomarkRNAs.txt')
biomark.rnas <- biomark.rnas$V1
```

use biomark RNAs to define dimensional reduction
```{r}
cmp.object <- readRDS("CMP_raw.RDS")
cmp.object <- RunPCA(cmp.object, features = biomark.rnas, ndims.print = 1:5, , nfeatures.print = 5)
ElbowPlot(cmp.object, ndims = 50)
```


Now run the clustering
```{r}
tot.var <- percent.variance(cmp.object@reductions$pca@stdev, plot.var = FALSE, return.val = TRUE)
ndims <- length(which(cumsum(tot.var) <= 90))

cmp.object <- FindNeighbors(cmp.object, dims = 1:ndims)
cmp.object <- FindClusters(cmp.object, resolution = 0.5)
cmp.object <- RunUMAP(cmp.object, dims = 1: ndims)


```

find the clusters

```{r}
for(x in c(0.5, 1, 1.5, 2, 2.5)){
	cmp.object <- FindClusters(cmp.object, resolution = x)
}
```

Plot the umaps and cell cluster ids
```{r}
for (meta.col in colnames(cmp.object@meta.data)){
	if(grepl(pattern = ("RNA_snn_res"), x = meta.col)==TRUE){
		myplot <- DimPlot(cmp.object, 
											group.by = meta.col,
											reduction = "umap", 
											cols = color.palette
											) + 
			ggtitle(paste0(projectName, " dim", ndims, "res", gsub("RNA_snn_res", "", meta.col) ))
		plot(myplot)
	}
}
```
### Calculate anticipated number of cells you'll find in each biomark cluster
Get # cells in each cluster

```{r}
tot.cellcount <- nrow(cmp.object@meta.data)
res05.list <- sort(unique(cmp.object@meta.data$RNA_snn_res.0.5), decreasing = FALSE)
sapply(res05.list, 
			 function(x){
			 	print(
			 		paste(
			 			"cluster", x, "=", 
			 			nrow(cmp.object@meta.data[cmp.object@meta.data$RNA_snn_res.0.5 == x,]), 
			 			"cells or", 
			 			round(nrow(cmp.object@meta.data[cmp.object@meta.data$RNA_snn_res.0.5 == x,])/tot.cellcount*100, digits = 2), 
			 			"% of total"
			 		)
			 	)
			 }
			)
```

So we did the dimensional reduction based on the biomark RNAs, then did our UMAP nearest neighbor clustering.


In the biomark hierarchcial clustering analysis I assayed 167 cells. The smallest cluster I detected had 3 cells, or 1.8% of total, and this is an uncomfortably small number of cells. Based on the UMAP calculations I would therefore expect to find 11 or 12 of the predicted 15 clusters. I found 12, and I don't really like that last one, so 11 or 12. Since I did the hierarchcial clustering yesterday and did this math today, we can say it was independent of these results and therefore totally legit. Yay!!

